Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory76.9 B

Variable types

Numeric8

Dataset

Description국내 무연탄 수급에 대한 데이터로, 국내에서 생산된 석탄(무연탄)의 연도별 생산, 소비, 재고 등 수급 동향 정보 제공
URLhttps://www.data.go.kr/data/15052387/fileData.do

Alerts

연도 is highly overall correlated with 생산(천톤) and 5 other fieldsHigh correlation
생산(천톤) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
소비(천톤) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
연탄용소비(천톤) is highly overall correlated with 소비(천톤)High correlation
발전용소비(천톤) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
산업용소비(천톤) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
재고(천톤) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
정부비축(천톤) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
연도 has unique valuesUnique
소비(천톤) has unique valuesUnique
연탄용소비(천톤) has unique valuesUnique
재고(천톤) has unique valuesUnique
산업용소비(천톤) has 13 (48.1%) zerosZeros

Reproduction

Analysis started2023-12-12 20:50:36.298193
Analysis finished2023-12-12 20:50:43.909252
Duration7.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009
Minimum1996
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:43.989753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile1997.3
Q12002.5
median2009
Q32015.5
95-th percentile2020.7
Maximum2022
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.0039508481
Kurtosis-1.2
Mean2009
Median Absolute Deviation (MAD)7
Skewness0
Sum54243
Variance63
MonotonicityStrictly increasing
2023-12-13T05:50:44.162256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1996 1
 
3.7%
1997 1
 
3.7%
2022 1
 
3.7%
2021 1
 
3.7%
2020 1
 
3.7%
2019 1
 
3.7%
2018 1
 
3.7%
2017 1
 
3.7%
2016 1
 
3.7%
2015 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1996 1
3.7%
1997 1
3.7%
1998 1
3.7%
1999 1
3.7%
2000 1
3.7%
2001 1
3.7%
2002 1
3.7%
2003 1
3.7%
2004 1
3.7%
2005 1
3.7%
ValueCountFrequency (%)
2022 1
3.7%
2021 1
3.7%
2020 1
3.7%
2019 1
3.7%
2018 1
3.7%
2017 1
3.7%
2016 1
3.7%
2015 1
3.7%
2014 1
3.7%
2013 1
3.7%

생산(천톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2572.3704
Minimum820
Maximum4951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:44.353564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum820
5-th percentile934.3
Q11737
median2519
Q33308.5
95-th percentile4468.1
Maximum4951
Range4131
Interquartile range (IQR)1571.5

Descriptive statistics

Standard deviation1207.8995
Coefficient of variation (CV)0.46956671
Kurtosis-0.9301118
Mean2572.3704
Median Absolute Deviation (MAD)793
Skewness0.33701115
Sum69454
Variance1459021.2
MonotonicityNot monotonic
2023-12-13T05:50:44.520951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2084 2
 
7.4%
4951 1
 
3.7%
820 1
 
3.7%
898 1
 
3.7%
1019 1
 
3.7%
1084 1
 
3.7%
1202 1
 
3.7%
1485 1
 
3.7%
1726 1
 
3.7%
1764 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
820 1
3.7%
898 1
3.7%
1019 1
3.7%
1084 1
3.7%
1202 1
3.7%
1485 1
3.7%
1726 1
3.7%
1748 1
3.7%
1764 1
3.7%
1815 1
3.7%
ValueCountFrequency (%)
4951 1
3.7%
4514 1
3.7%
4361 1
3.7%
4197 1
3.7%
4150 1
3.7%
3817 1
3.7%
3318 1
3.7%
3299 1
3.7%
3191 1
3.7%
2886 1
3.7%

소비(천톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2871.4444
Minimum825
Maximum4716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:44.672182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum825
5-th percentile866.7
Q11606.5
median3309
Q33983.5
95-th percentile4428
Maximum4716
Range3891
Interquartile range (IQR)2377

Descriptive statistics

Standard deviation1342.5119
Coefficient of variation (CV)0.46753887
Kurtosis-1.5615295
Mean2871.4444
Median Absolute Deviation (MAD)951
Skewness-0.2887087
Sum77529
Variance1802338.2
MonotonicityNot monotonic
2023-12-13T05:50:44.826469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4337 1
 
3.7%
3769 1
 
3.7%
825 1
 
3.7%
849 1
 
3.7%
908 1
 
3.7%
1044 1
 
3.7%
1143 1
 
3.7%
1314 1
 
3.7%
1495 1
 
3.7%
1718 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
825 1
3.7%
849 1
3.7%
908 1
3.7%
1044 1
3.7%
1143 1
3.7%
1314 1
3.7%
1495 1
3.7%
1718 1
3.7%
1879 1
3.7%
2240 1
3.7%
ValueCountFrequency (%)
4716 1
3.7%
4467 1
3.7%
4337 1
3.7%
4260 1
3.7%
4254 1
3.7%
4159 1
3.7%
4026 1
3.7%
3941 1
3.7%
3886 1
3.7%
3853 1
3.7%

연탄용소비(천톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1419.7037
Minimum425
Maximum2327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:44.966322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum425
5-th percentile466.7
Q11146
median1385
Q31888
95-th percentile2229.6
Maximum2327
Range1902
Interquartile range (IQR)742

Descriptive statistics

Standard deviation549.68346
Coefficient of variation (CV)0.38718182
Kurtosis-0.7747914
Mean1419.7037
Median Absolute Deviation (MAD)448
Skewness-0.20342408
Sum38332
Variance302151.91
MonotonicityNot monotonic
2023-12-13T05:50:45.098281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1960 1
 
3.7%
1389 1
 
3.7%
425 1
 
3.7%
449 1
 
3.7%
508 1
 
3.7%
644 1
 
3.7%
913 1
 
3.7%
1079 1
 
3.7%
1255 1
 
3.7%
1473 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
425 1
3.7%
449 1
3.7%
508 1
3.7%
644 1
3.7%
913 1
3.7%
1079 1
3.7%
1117 1
3.7%
1175 1
3.7%
1191 1
3.7%
1192 1
3.7%
ValueCountFrequency (%)
2327 1
3.7%
2289 1
3.7%
2091 1
3.7%
2010 1
3.7%
1960 1
3.7%
1941 1
3.7%
1917 1
3.7%
1859 1
3.7%
1833 1
3.7%
1822 1
3.7%

발전용소비(천톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1417.963
Minimum230
Maximum2850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:45.245669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum230
5-th percentile236.5
Q1400
median1360
Q32403.5
95-th percentile2703.7
Maximum2850
Range2620
Interquartile range (IQR)2003.5

Descriptive statistics

Standard deviation1047.9569
Coefficient of variation (CV)0.73905804
Kurtosis-1.956564
Mean1417.963
Median Absolute Deviation (MAD)996
Skewness0.041297914
Sum38285
Variance1098213.7
MonotonicityNot monotonic
2023-12-13T05:50:45.353774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
400 4
 
14.8%
2356 2
 
7.4%
2514 1
 
3.7%
839 1
 
3.7%
230 1
 
3.7%
235 1
 
3.7%
240 1
 
3.7%
245 1
 
3.7%
250 1
 
3.7%
323 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
230 1
 
3.7%
235 1
 
3.7%
240 1
 
3.7%
245 1
 
3.7%
250 1
 
3.7%
323 1
 
3.7%
400 4
14.8%
543 1
 
3.7%
591 1
 
3.7%
839 1
 
3.7%
ValueCountFrequency (%)
2850 1
3.7%
2710 1
3.7%
2689 1
3.7%
2558 1
3.7%
2552 1
3.7%
2514 1
3.7%
2451 1
3.7%
2356 2
7.4%
2354 1
3.7%
2323 1
3.7%

산업용소비(천톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40
Minimum0
Maximum184
Zeros13
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:45.481863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q366
95-th percentile156.9
Maximum184
Range184
Interquartile range (IQR)66

Descriptive statistics

Standard deviation57.680153
Coefficient of variation (CV)1.4420038
Kurtosis0.48132686
Mean40
Median Absolute Deviation (MAD)7
Skewness1.3176699
Sum1080
Variance3327
MonotonicityNot monotonic
2023-12-13T05:50:45.616390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 13
48.1%
28 1
 
3.7%
57 1
 
3.7%
162 1
 
3.7%
184 1
 
3.7%
117 1
 
3.7%
107 1
 
3.7%
75 1
 
3.7%
43 1
 
3.7%
145 1
 
3.7%
Other values (5) 5
 
18.5%
ValueCountFrequency (%)
0 13
48.1%
7 1
 
3.7%
8 1
 
3.7%
11 1
 
3.7%
28 1
 
3.7%
33 1
 
3.7%
43 1
 
3.7%
57 1
 
3.7%
75 1
 
3.7%
103 1
 
3.7%
ValueCountFrequency (%)
184 1
3.7%
162 1
3.7%
145 1
3.7%
117 1
3.7%
107 1
3.7%
103 1
3.7%
75 1
3.7%
57 1
3.7%
43 1
3.7%
33 1
3.7%

재고(천톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5185.7037
Minimum1457
Maximum10774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:45.759183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1457
5-th percentile1615.4
Q12188.5
median2807
Q39232
95-th percentile10688.7
Maximum10774
Range9317
Interquartile range (IQR)7043.5

Descriptive statistics

Standard deviation3667.3024
Coefficient of variation (CV)0.70719474
Kurtosis-1.6219498
Mean5185.7037
Median Absolute Deviation (MAD)1197
Skewness0.52044465
Sum140014
Variance13449107
MonotonicityNot monotonic
2023-12-13T05:50:45.899090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8937 1
 
3.7%
9720 1
 
3.7%
2807 1
 
3.7%
2810 1
 
3.7%
2772 1
 
3.7%
2650 1
 
3.7%
2589 1
 
3.7%
2441 1
 
3.7%
2151 1
 
3.7%
1798 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1457 1
3.7%
1610 1
3.7%
1628 1
3.7%
1720 1
3.7%
1798 1
3.7%
1853 1
3.7%
2151 1
3.7%
2226 1
3.7%
2441 1
3.7%
2589 1
3.7%
ValueCountFrequency (%)
10774 1
3.7%
10737 1
3.7%
10576 1
3.7%
10269 1
3.7%
10101 1
3.7%
9720 1
3.7%
9527 1
3.7%
8937 1
3.7%
8894 1
3.7%
7388 1
3.7%

정부비축(천톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3619.8519
Minimum899
Maximum8111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:50:46.028124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum899
5-th percentile899
Q1928
median1596
Q36983.5
95-th percentile8111
Maximum8111
Range7212
Interquartile range (IQR)6055.5

Descriptive statistics

Standard deviation3080.0863
Coefficient of variation (CV)0.85088739
Kurtosis-1.6222252
Mean3619.8519
Median Absolute Deviation (MAD)697
Skewness0.51264833
Sum97736
Variance9486931.7
MonotonicityNot monotonic
2023-12-13T05:50:46.158213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
899 4
 
14.8%
8111 3
 
11.1%
8061 2
 
7.4%
5033 1
 
3.7%
1142 1
 
3.7%
955 1
 
3.7%
944 1
 
3.7%
932 1
 
3.7%
918 1
 
3.7%
905 1
 
3.7%
Other values (11) 11
40.7%
ValueCountFrequency (%)
899 4
14.8%
905 1
 
3.7%
918 1
 
3.7%
924 1
 
3.7%
932 1
 
3.7%
944 1
 
3.7%
955 1
 
3.7%
1080 1
 
3.7%
1142 1
 
3.7%
1308 1
 
3.7%
ValueCountFrequency (%)
8111 3
11.1%
8061 2
7.4%
7801 1
 
3.7%
7024 1
 
3.7%
6943 1
 
3.7%
6038 1
 
3.7%
5033 1
 
3.7%
4671 1
 
3.7%
3445 1
 
3.7%
2026 1
 
3.7%

Interactions

2023-12-13T05:50:42.371760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:36.571096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:37.743772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.539122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.343277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.151639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.846513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.603091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.466257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:36.681183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:37.846696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.647354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.453957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.242436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.930288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.684479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.558887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:36.776220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:37.961717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.756037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.550731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.326478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.009988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.757688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.682397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:36.880760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.056539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.875404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.643436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.427756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.104339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.852839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.801804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:36.986867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.163171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.978528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.745218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.522719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.209833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.964666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.930558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:37.102575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.236933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.054814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.834049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.607575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.292160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.069166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:43.035292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:37.206416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.336260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.155124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.944546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.696092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.378342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.157035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:43.144701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:37.632710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:38.424045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:39.239742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.036553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:40.760856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:41.475854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:42.254010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:50:46.290466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생산(천톤)소비(천톤)연탄용소비(천톤)발전용소비(천톤)산업용소비(천톤)재고(천톤)정부비축(천톤)
연도1.0000.8670.7040.7400.6760.7330.7230.612
생산(천톤)0.8671.0000.7890.6590.5970.7260.7550.520
소비(천톤)0.7040.7891.0000.6970.8130.0000.4690.736
연탄용소비(천톤)0.7400.6590.6971.0000.3520.0000.5900.646
발전용소비(천톤)0.6760.5970.8130.3521.0000.0000.5620.791
산업용소비(천톤)0.7330.7260.0000.0000.0001.0000.7890.818
재고(천톤)0.7230.7550.4690.5900.5620.7891.0000.812
정부비축(천톤)0.6120.5200.7360.6460.7910.8180.8121.000
2023-12-13T05:50:46.439651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생산(천톤)소비(천톤)연탄용소비(천톤)발전용소비(천톤)산업용소비(천톤)재고(천톤)정부비축(천톤)
연도1.000-0.996-0.833-0.377-0.863-0.875-0.708-0.844
생산(천톤)-0.9961.0000.8270.3750.8590.8680.7050.839
소비(천톤)-0.8330.8271.0000.6630.7890.7600.5520.752
연탄용소비(천톤)-0.3770.3750.6631.0000.1760.132-0.2070.130
발전용소비(천톤)-0.8630.8590.7890.1761.0000.8790.8260.970
산업용소비(천톤)-0.8750.8680.7600.1320.8791.0000.8680.904
재고(천톤)-0.7080.7050.552-0.2070.8260.8681.0000.841
정부비축(천톤)-0.8440.8390.7520.1300.9700.9040.8411.000

Missing values

2023-12-13T05:50:43.333108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:50:43.848681image/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

연도생산(천톤)소비(천톤)연탄용소비(천톤)발전용소비(천톤)산업용소비(천톤)재고(천톤)정부비축(천톤)
0199649514337196025142889375033
1199745143769138923235797206038
219984361384212292451162102697024
319994197385311172552184107377801
420004150415911922850117107748111
520013817402612302689107105768111
62002331838081175255875101018111
7200332993941119127104395278061
82004319138861385235614588948061
92005283244672010235410373886943
연도생산(천톤)소비(천톤)연탄용소비(천톤)발전용소비(천톤)산업용소비(천톤)재고(천톤)정부비축(천톤)
17201318152240191732301457924
18201417481879162925001610899
19201517641718147324501798899
20201617261495125524002151899
21201714851314107923502441899
2220181202114391323002589905
2320191084104464440002650918
242020101990850840002772932
25202189884944940002810944
26202282082542540002807955