Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory126.3 B

Variable types

Numeric13
Categorical1

Dataset

Description한국광해광업공단은 석탄산업의 생산 기반 유지와 연탄의 안정적인 공급을 위해 석·연탄산업 지원 사업을 실시하고 있으며 이를 통해 자원안보와 서민생활보호 및 폐광지역 고용창출 등에 이바지하고 있습니다. 무연탄 소비현황을 부분별, 계절별, 월별, 지역별 등으로 구분하여 정보 제공합니다.
URLhttps://www.data.go.kr/data/15068068/fileData.do

Alerts

연도 is highly overall correlated with 1월(천톤) and 11 other fieldsHigh correlation
1월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
2월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
3월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
4월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
5월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
6월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
7월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
8월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
9월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
10월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
11월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
12월(천톤) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
1월(천톤) has 14 (14.0%) zerosZeros
2월(천톤) has 15 (15.0%) zerosZeros
3월(천톤) has 15 (15.0%) zerosZeros
4월(천톤) has 15 (15.0%) zerosZeros
5월(천톤) has 15 (15.0%) zerosZeros
6월(천톤) has 15 (15.0%) zerosZeros
7월(천톤) has 15 (15.0%) zerosZeros
8월(천톤) has 14 (14.0%) zerosZeros
9월(천톤) has 14 (14.0%) zerosZeros
10월(천톤) has 14 (14.0%) zerosZeros
11월(천톤) has 15 (15.0%) zerosZeros
12월(천톤) has 16 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-12 05:50:46.659588
Analysis finished2023-12-12 05:51:06.418316
Duration19.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.58
Minimum1981
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:06.502604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1981
5-th percentile1983
Q11993
median2005.5
Q32014
95-th percentile2021
Maximum2022
Range41
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.289587
Coefficient of variation (CV)0.0061338138
Kurtosis-1.1797834
Mean2003.58
Median Absolute Deviation (MAD)10.5
Skewness-0.25427485
Sum200358
Variance151.03394
MonotonicityIncreasing
2023-12-12T14:51:06.644265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2014 3
 
3.0%
2016 3
 
3.0%
2008 3
 
3.0%
2009 3
 
3.0%
2010 3
 
3.0%
2011 3
 
3.0%
2012 3
 
3.0%
2013 3
 
3.0%
2015 3
 
3.0%
2017 3
 
3.0%
Other values (32) 70
70.0%
ValueCountFrequency (%)
1981 2
2.0%
1982 2
2.0%
1983 2
2.0%
1984 2
2.0%
1985 2
2.0%
1986 2
2.0%
1987 2
2.0%
1988 2
2.0%
1989 2
2.0%
1990 2
2.0%
ValueCountFrequency (%)
2022 3
3.0%
2021 3
3.0%
2020 3
3.0%
2019 3
3.0%
2018 3
3.0%
2017 3
3.0%
2016 3
3.0%
2015 3
3.0%
2014 3
3.0%
2013 3
3.0%

구분
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
가정상업
42 
발전
42 
산업
16 

Length

Max length4
Median length2
Mean length2.84
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가정상업
2nd row발전
3rd row가정상업
4th row발전
5th row가정상업

Common Values

ValueCountFrequency (%)
가정상업 42
42.0%
발전 42
42.0%
산업 16
 
16.0%

Length

2023-12-12T14:51:06.768972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:51:06.877789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가정상업 42
42.0%
발전 42
42.0%
산업 16
 
16.0%

1월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.46
Minimum0
Maximum3152
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:06.991825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136
median161.5
Q3227.5
95-th percentile2732
Maximum3152
Range3152
Interquartile range (IQR)191.5

Descriptive statistics

Standard deviation785.20304
Coefficient of variation (CV)1.8674857
Kurtosis4.5147208
Mean420.46
Median Absolute Deviation (MAD)100.5
Skewness2.4348111
Sum42046
Variance616543.81
MonotonicityNot monotonic
2023-12-12T14:51:07.126425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
14.0%
170 3
 
3.0%
214 2
 
2.0%
1 2
 
2.0%
21 2
 
2.0%
15 2
 
2.0%
2732 2
 
2.0%
248 2
 
2.0%
219 2
 
2.0%
160 2
 
2.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0 14
14.0%
1 2
 
2.0%
15 2
 
2.0%
17 1
 
1.0%
18 1
 
1.0%
20 1
 
1.0%
21 2
 
2.0%
30 1
 
1.0%
33 1
 
1.0%
37 1
 
1.0%
ValueCountFrequency (%)
3152 1
1.0%
2926 1
1.0%
2765 1
1.0%
2756 1
1.0%
2732 2
2.0%
2550 1
1.0%
2266 1
1.0%
2256 1
1.0%
2092 1
1.0%
1777 1
1.0%

2월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345.885
Minimum0
Maximum2659
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:07.254311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134.5
median137.5
Q3191.75
95-th percentile2107.3
Maximum2659
Range2659
Interquartile range (IQR)157.25

Descriptive statistics

Standard deviation652.41788
Coefficient of variation (CV)1.8862277
Kurtosis5.0516598
Mean345.885
Median Absolute Deviation (MAD)81.5
Skewness2.5143335
Sum34588.5
Variance425649.09
MonotonicityNot monotonic
2023-12-12T14:51:07.380379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
15.0 3
 
3.0%
143.0 3
 
3.0%
113.0 2
 
2.0%
191.0 2
 
2.0%
294.0 2
 
2.0%
139.0 2
 
2.0%
221.0 2
 
2.0%
2659.0 2
 
2.0%
194.0 2
 
2.0%
Other values (62) 65
65.0%
ValueCountFrequency (%)
0.0 15
15.0%
1.5 1
 
1.0%
6.0 1
 
1.0%
11.0 1
 
1.0%
15.0 3
 
3.0%
18.0 1
 
1.0%
25.0 2
 
2.0%
33.0 1
 
1.0%
35.0 2
 
2.0%
38.0 1
 
1.0%
ValueCountFrequency (%)
2659.0 2
2.0%
2410.0 1
1.0%
2392.0 1
1.0%
2227.0 1
1.0%
2101.0 1
1.0%
2043.0 1
1.0%
1895.0 1
1.0%
1676.0 1
1.0%
1580.0 1
1.0%
1568.0 1
1.0%

3월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.81
Minimum0
Maximum2606
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:07.532254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127.5
median126.5
Q3205.25
95-th percentile2220.2
Maximum2606
Range2606
Interquartile range (IQR)177.75

Descriptive statistics

Standard deviation661.03532
Coefficient of variation (CV)1.8630685
Kurtosis4.6426445
Mean354.81
Median Absolute Deviation (MAD)90.5
Skewness2.4445789
Sum35481
Variance436967.69
MonotonicityNot monotonic
2023-12-12T14:51:07.669907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
96 2
 
2.0%
194 2
 
2.0%
178 2
 
2.0%
2606 2
 
2.0%
2300 2
 
2.0%
1 2
 
2.0%
187 2
 
2.0%
15 2
 
2.0%
209 1
 
1.0%
Other values (68) 68
68.0%
ValueCountFrequency (%)
0 15
15.0%
1 2
 
2.0%
11 1
 
1.0%
15 2
 
2.0%
18 1
 
1.0%
20 1
 
1.0%
22 1
 
1.0%
25 1
 
1.0%
26 1
 
1.0%
28 1
 
1.0%
ValueCountFrequency (%)
2606 2
2.0%
2430 1
1.0%
2300 2
2.0%
2216 1
1.0%
2183 1
1.0%
1862 1
1.0%
1767 1
1.0%
1714 1
1.0%
1605 1
1.0%
1090 1
1.0%

4월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.02
Minimum0
Maximum2002
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:07.812475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.5
median89.5
Q3202
95-th percentile1517.8
Maximum2002
Range2002
Interquartile range (IQR)175.5

Descriptive statistics

Standard deviation497.92103
Coefficient of variation (CV)1.7974191
Kurtosis4.5512624
Mean277.02
Median Absolute Deviation (MAD)89
Skewness2.4097806
Sum27702
Variance247925.35
MonotonicityNot monotonic
2023-12-12T14:51:07.957132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
39 2
 
2.0%
19 2
 
2.0%
104 2
 
2.0%
54 2
 
2.0%
183 2
 
2.0%
34 2
 
2.0%
23 2
 
2.0%
1835 2
 
2.0%
202 2
 
2.0%
Other values (63) 67
67.0%
ValueCountFrequency (%)
0 15
15.0%
1 1
 
1.0%
17 1
 
1.0%
19 2
 
2.0%
21 1
 
1.0%
23 2
 
2.0%
24 1
 
1.0%
25 2
 
2.0%
27 1
 
1.0%
28 2
 
2.0%
ValueCountFrequency (%)
2002 1
1.0%
1922 1
1.0%
1835 2
2.0%
1818 1
1.0%
1502 1
1.0%
1495 1
1.0%
1437 1
1.0%
1430 1
1.0%
1282 1
1.0%
1198 1
1.0%

5월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.26
Minimum0
Maximum1204
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:08.096153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120.5
median54.5
Q3205.25
95-th percentile1003.3
Maximum1204
Range1204
Interquartile range (IQR)184.75

Descriptive statistics

Standard deviation307.64136
Coefficient of variation (CV)1.5362097
Kurtosis3.257096
Mean200.26
Median Absolute Deviation (MAD)54.5
Skewness2.1006518
Sum20026
Variance94643.204
MonotonicityNot monotonic
2023-12-12T14:51:08.227690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
35 3
 
3.0%
24 3
 
3.0%
214 2
 
2.0%
196 2
 
2.0%
1047 2
 
2.0%
64 2
 
2.0%
18 2
 
2.0%
19 2
 
2.0%
45 2
 
2.0%
Other values (61) 65
65.0%
ValueCountFrequency (%)
0 15
15.0%
1 1
 
1.0%
7 1
 
1.0%
9 1
 
1.0%
10 2
 
2.0%
13 1
 
1.0%
18 2
 
2.0%
19 2
 
2.0%
21 1
 
1.0%
24 3
 
3.0%
ValueCountFrequency (%)
1204 1
1.0%
1143 1
1.0%
1086 1
1.0%
1047 2
2.0%
1001 1
1.0%
950 1
1.0%
949 1
1.0%
932 1
1.0%
882 1
1.0%
860 1
1.0%

6월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.59
Minimum0
Maximum1524
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:08.372691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.75
median49.5
Q3207
95-th percentile578.1
Maximum1524
Range1524
Interquartile range (IQR)185.25

Descriptive statistics

Standard deviation226.92268
Coefficient of variation (CV)1.4491518
Kurtosis13.040362
Mean156.59
Median Absolute Deviation (MAD)49.5
Skewness2.9968418
Sum15659
Variance51493.901
MonotonicityNot monotonic
2023-12-12T14:51:08.757925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
23 5
 
5.0%
14 3
 
3.0%
205 3
 
3.0%
49 3
 
3.0%
212 2
 
2.0%
33 2
 
2.0%
216 2
 
2.0%
577 2
 
2.0%
29 2
 
2.0%
Other values (56) 61
61.0%
ValueCountFrequency (%)
0 15
15.0%
1 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 2
 
2.0%
14 3
 
3.0%
21 2
 
2.0%
22 1
 
1.0%
23 5
 
5.0%
26 2
 
2.0%
ValueCountFrequency (%)
1524 1
1.0%
776 1
1.0%
682 1
1.0%
629 1
1.0%
599 1
1.0%
577 2
2.0%
566 1
1.0%
563 1
1.0%
541 1
1.0%
476 1
1.0%

7월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.81
Minimum0
Maximum787
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:08.905407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median47.5
Q3201
95-th percentile362.75
Maximum787
Range787
Interquartile range (IQR)186

Descriptive statistics

Standard deviation144.04823
Coefficient of variation (CV)1.2124251
Kurtosis4.3267467
Mean118.81
Median Absolute Deviation (MAD)47.5
Skewness1.7931773
Sum11881
Variance20749.893
MonotonicityNot monotonic
2023-12-12T14:51:09.066752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
19 3
 
3.0%
15 3
 
3.0%
27 3
 
3.0%
18 3
 
3.0%
60 2
 
2.0%
296 2
 
2.0%
17 2
 
2.0%
23 2
 
2.0%
30 2
 
2.0%
Other values (59) 63
63.0%
ValueCountFrequency (%)
0 15
15.0%
1 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
7 2
 
2.0%
8 1
 
1.0%
11 1
 
1.0%
14 1
 
1.0%
15 3
 
3.0%
17 2
 
2.0%
ValueCountFrequency (%)
787 1
1.0%
517 1
1.0%
510 1
1.0%
491 1
1.0%
434 1
1.0%
359 1
1.0%
355 1
1.0%
333 1
1.0%
297 1
1.0%
296 2
2.0%

8월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.435
Minimum0
Maximum1119
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:09.201137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124.25
median54.5
Q3205
95-th percentile530.05
Maximum1119
Range1119
Interquartile range (IQR)180.75

Descriptive statistics

Standard deviation183.27832
Coefficient of variation (CV)1.3239305
Kurtosis8.2829469
Mean138.435
Median Absolute Deviation (MAD)54.5
Skewness2.473886
Sum13843.5
Variance33590.943
MonotonicityNot monotonic
2023-12-12T14:51:09.318363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
27.0 3
 
3.0%
36.0 3
 
3.0%
28.0 2
 
2.0%
205.0 2
 
2.0%
51.0 2
 
2.0%
45.0 2
 
2.0%
209.0 2
 
2.0%
516.0 2
 
2.0%
25.0 2
 
2.0%
Other values (65) 66
66.0%
ValueCountFrequency (%)
0.0 14
14.0%
1.0 1
 
1.0%
1.5 1
 
1.0%
6.0 1
 
1.0%
8.0 1
 
1.0%
9.0 1
 
1.0%
10.0 1
 
1.0%
12.0 1
 
1.0%
16.0 1
 
1.0%
18.0 1
 
1.0%
ValueCountFrequency (%)
1119.0 1
1.0%
638.0 1
1.0%
609.0 1
1.0%
573.0 1
1.0%
531.0 1
1.0%
530.0 1
1.0%
516.0 2
2.0%
513.0 1
1.0%
381.0 1
1.0%
327.0 1
1.0%

9월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.01
Minimum0
Maximum2151
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:09.464770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.75
median130
Q3197
95-th percentile1400.65
Maximum2151
Range2151
Interquartile range (IQR)165.25

Descriptive statistics

Standard deviation427.60431
Coefficient of variation (CV)1.6637653
Kurtosis6.3407941
Mean257.01
Median Absolute Deviation (MAD)83.5
Skewness2.6233448
Sum25701
Variance182845.44
MonotonicityNot monotonic
2023-12-12T14:51:09.599913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
14.0%
21 4
 
4.0%
192 3
 
3.0%
1 2
 
2.0%
1470 2
 
2.0%
161 2
 
2.0%
33 2
 
2.0%
157 2
 
2.0%
197 2
 
2.0%
73 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0 14
14.0%
1 2
 
2.0%
13 1
 
1.0%
15 1
 
1.0%
21 4
 
4.0%
22 1
 
1.0%
25 1
 
1.0%
31 1
 
1.0%
32 1
 
1.0%
33 2
 
2.0%
ValueCountFrequency (%)
2151 1
1.0%
1750 1
1.0%
1505 1
1.0%
1470 2
2.0%
1397 1
1.0%
1293 1
1.0%
1161 1
1.0%
1133 1
1.0%
1067 1
1.0%
780 1
1.0%

10월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.48
Minimum0
Maximum3426
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:09.726318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132.25
median179
Q3267.5
95-th percentile2888.6
Maximum3426
Range3426
Interquartile range (IQR)235.25

Descriptive statistics

Standard deviation832.47852
Coefficient of variation (CV)1.8479811
Kurtosis4.8424602
Mean450.48
Median Absolute Deviation (MAD)118
Skewness2.4806112
Sum45048
Variance693020.49
MonotonicityNot monotonic
2023-12-12T14:51:09.847175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
14.0%
177 3
 
3.0%
176 2
 
2.0%
253 2
 
2.0%
1 2
 
2.0%
18 2
 
2.0%
2938 2
 
2.0%
184 2
 
2.0%
102 1
 
1.0%
40 1
 
1.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0 14
14.0%
1 2
 
2.0%
9 1
 
1.0%
13 1
 
1.0%
15 1
 
1.0%
18 2
 
2.0%
21 1
 
1.0%
22 1
 
1.0%
26 1
 
1.0%
30 1
 
1.0%
ValueCountFrequency (%)
3426 1
1.0%
3184 1
1.0%
2953 1
1.0%
2938 2
2.0%
2886 1
1.0%
2821 1
1.0%
2507 1
1.0%
2241 1
1.0%
1874 1
1.0%
1723 1
1.0%

11월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.22
Minimum0
Maximum3143
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:09.965737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138.75
median178
Q3300
95-th percentile2824.1
Maximum3143
Range3143
Interquartile range (IQR)261.25

Descriptive statistics

Standard deviation813.96522
Coefficient of variation (CV)1.8079277
Kurtosis4.844821
Mean450.22
Median Absolute Deviation (MAD)128
Skewness2.487987
Sum45022
Variance662539.39
MonotonicityNot monotonic
2023-12-12T14:51:10.103477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
15.0%
303 2
 
2.0%
3066 2
 
2.0%
275 2
 
2.0%
85 2
 
2.0%
175 2
 
2.0%
174 2
 
2.0%
22 2
 
2.0%
178 2
 
2.0%
77 1
 
1.0%
Other values (68) 68
68.0%
ValueCountFrequency (%)
0 15
15.0%
1 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
17 1
 
1.0%
20 1
 
1.0%
22 2
 
2.0%
31 1
 
1.0%
33 1
 
1.0%
35 1
 
1.0%
ValueCountFrequency (%)
3143 1
1.0%
3088 1
1.0%
3066 2
2.0%
2864 1
1.0%
2822 1
1.0%
2752 1
1.0%
2432 1
1.0%
2216 1
1.0%
2065 1
1.0%
1728 1
1.0%

12월(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.29
Minimum0
Maximum3544
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:51:10.268412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median181.5
Q3271
95-th percentile3163.1
Maximum3544
Range3544
Interquartile range (IQR)229

Descriptive statistics

Standard deviation884.68081
Coefficient of variation (CV)1.9178408
Kurtosis5.3566179
Mean461.29
Median Absolute Deviation (MAD)108
Skewness2.5851259
Sum46129
Variance782660.13
MonotonicityNot monotonic
2023-12-12T14:51:10.408909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
16.0%
15 3
 
3.0%
3184 2
 
2.0%
195 2
 
2.0%
207 2
 
2.0%
194 2
 
2.0%
281 2
 
2.0%
18 2
 
2.0%
2227 1
 
1.0%
115 1
 
1.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0 16
16.0%
1 1
 
1.0%
6 1
 
1.0%
15 3
 
3.0%
18 2
 
2.0%
29 1
 
1.0%
33 1
 
1.0%
45 1
 
1.0%
49 1
 
1.0%
60 1
 
1.0%
ValueCountFrequency (%)
3544 1
1.0%
3487 1
1.0%
3330 1
1.0%
3184 2
2.0%
3162 1
1.0%
2842 1
1.0%
2599 1
1.0%
2289 1
1.0%
2227 1
1.0%
1582 1
1.0%

Interactions

2023-12-12T14:51:04.707719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.132277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.410242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.793154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.356040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.625295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.376175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.722852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.247429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.792332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.182843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.612541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:03.026903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.811550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.205524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.492567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.889787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.434272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.736742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.461241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.808545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.373418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.885392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.265276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.709721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:03.116841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.946836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.293987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.575119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.983356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.512161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.862750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.575805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.922864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.467978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.023808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.378637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.805232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:03.225232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.044604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.381282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.675896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:50.069903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.626533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.081472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.684278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.032153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.583916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.116507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.480277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.900017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:03.329029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.159052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.474535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.776507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:50.159935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.732689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.251385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.799096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.145144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.679700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.221749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.585680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.006017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:03.433231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.278573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.570473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.880853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:50.274669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.863867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.406394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.942571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.247551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.822334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.336701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.709307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.155441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:03.834093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.393881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.662920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.978906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:50.375253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.966192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.518811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.061681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.327729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.970743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.437526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.825579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.267735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:03.929815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.517053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.760004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.110714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:50.469790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.065271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.653693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.166293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.406196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.090182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.546210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.957432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.413336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.052122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.644300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.887074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.236910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:50.882593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.157271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.771077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.268725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.486936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.208505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.666191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.098168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.531066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.157131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.736725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:47.984936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.357034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:50.957678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.268596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.882146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.372395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.578885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.312231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.753228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.223327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.635507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.249767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.831665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.088980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.463806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.058122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.359478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:53.988006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.451783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.665855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.410207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.845872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.317723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.727493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.350923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:05.920341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.209861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.577212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.178714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.446235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.126874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.548853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:56.755166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.562360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:59.967769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.404329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.825270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.463247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:06.029615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:48.305327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:49.677444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:51.275247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:52.531204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:54.254019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:55.633188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:57.131267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:50:58.670764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:00.085689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:01.510181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:02.924718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:04.582984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:51:10.530050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분1월(천톤)2월(천톤)3월(천톤)4월(천톤)5월(천톤)6월(천톤)7월(천톤)8월(천톤)9월(천톤)10월(천톤)11월(천톤)12월(천톤)
연도1.0000.0970.5310.5380.5450.5740.6080.6060.5850.6150.5460.4620.5470.460
구분0.0971.0000.4720.4720.4620.4160.6290.6050.5590.6020.3930.4720.6350.435
1월(천톤)0.5310.4721.0000.9900.9920.9400.9000.8100.7630.8670.9700.9950.9830.987
2월(천톤)0.5380.4720.9901.0000.9750.9290.9010.8960.8080.8350.9740.9820.9900.988
3월(천톤)0.5450.4620.9920.9751.0000.9370.8870.7300.7570.7760.9580.9880.9820.974
4월(천톤)0.5740.4160.9400.9290.9371.0000.9670.7750.8670.7870.8990.9610.9560.939
5월(천톤)0.6080.6290.9000.9010.8870.9671.0000.8340.9090.8270.8790.8850.8750.917
6월(천톤)0.6060.6050.8100.8960.7300.7750.8341.0000.8880.9820.8610.7900.8610.863
7월(천톤)0.5850.5590.7630.8080.7570.8670.9090.8881.0000.9000.8200.7850.8480.775
8월(천톤)0.6150.6020.8670.8350.7760.7870.8270.9820.9001.0000.8710.8770.8430.851
9월(천톤)0.5460.3930.9700.9740.9580.8990.8790.8610.8200.8711.0000.9790.9740.972
10월(천톤)0.4620.4720.9950.9820.9880.9610.8850.7900.7850.8770.9791.0000.9880.987
11월(천톤)0.5470.6350.9830.9900.9820.9560.8750.8610.8480.8430.9740.9881.0000.991
12월(천톤)0.4600.4350.9870.9880.9740.9390.9170.8630.7750.8510.9720.9870.9911.000
2023-12-12T14:51:10.729444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도1월(천톤)2월(천톤)3월(천톤)4월(천톤)5월(천톤)6월(천톤)7월(천톤)8월(천톤)9월(천톤)10월(천톤)11월(천톤)12월(천톤)구분
연도1.000-0.688-0.732-0.785-0.825-0.821-0.787-0.741-0.784-0.812-0.643-0.603-0.6790.075
1월(천톤)-0.6881.0000.9700.9440.8790.8390.7910.7180.8160.9030.9090.9140.9250.228
2월(천톤)-0.7320.9701.0000.9710.9130.8710.8300.7620.8410.9190.8840.8800.8930.228
3월(천톤)-0.7850.9440.9711.0000.9490.9120.8730.8130.8770.9390.8470.8470.8760.221
4월(천톤)-0.8250.8790.9130.9491.0000.9740.9390.8850.9280.9560.8040.7890.8240.283
5월(천톤)-0.8210.8390.8710.9120.9741.0000.9710.9290.9600.9400.7550.7470.7900.487
6월(천톤)-0.7870.7910.8300.8730.9390.9711.0000.9640.9770.9050.7000.6970.7480.489
7월(천톤)-0.7410.7180.7620.8130.8850.9290.9641.0000.9510.8530.6390.6310.6850.413
8월(천톤)-0.7840.8160.8410.8770.9280.9600.9770.9511.0000.9140.7100.7170.7720.484
9월(천톤)-0.8120.9030.9190.9390.9560.9400.9050.8530.9141.0000.8620.8610.9050.181
10월(천톤)-0.6430.9090.8840.8470.8040.7550.7000.6390.7100.8621.0000.9660.9590.228
11월(천톤)-0.6030.9140.8800.8470.7890.7470.6970.6310.7170.8610.9661.0000.9690.341
12월(천톤)-0.6790.9250.8930.8760.8240.7900.7480.6850.7720.9050.9590.9691.0000.205
구분0.0750.2280.2280.2210.2830.4870.4890.4130.4840.1810.2280.3410.2051.000

Missing values

2023-12-12T14:51:06.160367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:51:06.342442image/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

연도구분1월(천톤)2월(천톤)3월(천톤)4월(천톤)5월(천톤)6월(천톤)7월(천톤)8월(천톤)9월(천톤)10월(천톤)11월(천톤)12월(천톤)
01981가정상업25501568.0221614959497767871119.01067172320652227
11981발전136136.093104134160200170.0192202166195
21982가정상업17771676.01714143012041524359381.01133187422162599
31982발전156175.0181214206205235169.0220186172207
41983가정상업20921895.018621282882566510609.01161250727522842
51983발전220179.0187178156157143145.0161177177194
61984가정상업27652101.0218314371001450491513.01505288628223162
71984발전192186.0194194200205204210.0173189147157
81985가정상업29262227.023001818860629434638.01397318431433544
91985발전151143.0158179184176179117.0110108133140
연도구분1월(천톤)2월(천톤)3월(천톤)4월(천톤)5월(천톤)6월(천톤)7월(천톤)8월(천톤)9월(천톤)10월(천톤)11월(천톤)12월(천톤)
902019산업00.0000000.00000
912020가정상업5848.0382510756.036958595
922020발전3333.0152824556041.01394049
932020산업00.0000000.00000
942021가정상업5638.028197668.032768588
952021발전4025.0152540556750.02115470
962021산업00.0000000.00000
972022가정상업5135.029179889.033708175
982022발전216.004570586060.025261415
992022산업00.0000000.00000